Senior Engineer at Tongji University, China
Yanjun Xu is a Senior Engineer and current Ph.D. candidate at Tongji University, Shanghai. With a strong foundation in operating systems and high-performance computing, he has contributed to advancing software-hardware co-simulation in robotics and optimizing kernel compilation processes. His work spans research, development, and practical innovation in virtual simulation and blockchain-integrated educational tools.
🔹Professional Profile:
Orcid Profile
🎓Education Background
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Bachelor’s Degree – Ocean University of China, 2010
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Master’s Degree – Xi’an Polytechnic University, 2013
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Ph.D. Candidate – Tongji University
💼 Professional Development
Yanjun Xu currently serves as a Senior Engineer at Tongji University. He has been actively involved in high-precision software and hardware co-simulation for robot development and has led virtual simulation projects. His work includes kernel-level system optimization, compiler enhancement, and integration of emerging technologies in real-world applications.
🔬Research Focus
📈Author Metrics:
🏆Awards and Honors:
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Active Member, Shanghai Blockchain Association
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Contributor to the Open Atom Foundation Community
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Recognized for contributions to open-source compiler development and system optimization
📝Publication Top Notes
📝 1. A Two-Way Dynamic Adaptive Pricing Resource Allocation Model Based on Combinatorial Double Auctions in Computational Network
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Journal: Computer Communications
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Publication Date: April 2025
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Type: Journal Article
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DOI: 10.1016/j.comcom.2025.108170
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Contributors: Yanjun Xu, Chunqi Tian, Wei Wang, Lizhi Bai, Xuhui Xia
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Source: Crossref
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Summary: This paper proposes a two-way dynamic adaptive pricing model for resource allocation using a combinatorial double auction mechanism in computational networks. The model is designed to handle the complexity of multi-party bidding and resource matching efficiently. By incorporating adaptive pricing strategies, the system dynamically balances supply and demand in real time, improving overall allocation efficiency. The method shows promising performance in cloud and edge computing scenarios, especially where resources are heterogeneously distributed and demand is volatile.
📝 2. Collaborative Motivation Framework Leveraging Similar Interest Behavior in Semantic Web Applications
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Journal: International Journal on Semantic Web and Information Systems (IJSWIS)
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Publication Date: December 28, 2024
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Type: Journal Article
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DOI: 10.4018/IJSWIS.365912
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Contributors: Yanjun Xu, Chunqi Tian, Yaoru Sun, Haodong Zhang
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Source: Crossref
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Summary: This research introduces a collaborative motivation framework that enhances user engagement in semantic web platforms by leveraging similar interest behavior. The proposed system models semantic relevance and behavioral similarity to personalize recommendations and group formation. This approach is particularly beneficial in social knowledge-sharing systems and e-learning platforms, where collaborative filtering alone may fall short. The framework shows strong potential for increasing user satisfaction and platform interactivity.
Conclusion:
Dr. Yanjun Xu exhibits all the hallmarks of an emerging global research leader. His technical depth, innovation in blockchain and systems research, publication record, and active community engagement make him a strong contender for the Best Researcher Award.
He bridges practical engineering challenges with advanced academic research, particularly in virtual simulation, semantic web systems, and blockchain-integrated education technologies. With continued growth in international collaborations and broader dissemination of his applied research, Dr. Xu is on a clear path to becoming a thought leader in computational systems and blockchain innovation.
✅ Recommendation: Highly suitable for the Best Researcher Award.